With the continuous development of economy and transportation, automobiles have become people's indispensable means of transportation. But with the constant improvement of automobile usage rate, traffic accidents also present a rising trend year by year. According to statistics, in all motor automobile accidents, the traffic accidents caused by lane departure account for twenty percent of all traffic accidents, therefore, in recent years, many research institutions at home and abroad have begun the study on automotive active safety technology.
The automotive active safety systems provided by the existing technology adopt radar and camera sensors, these systems judge the driving states of surrounding automobile through signal processing method; when finding there is a potential danger, they can judge in advance and avoid the occurrence of accident by means of warning and auxiliary brake.
But these studies have focused on passive receiving sensors like camera and radar, through the ways of signal processing and computer vision algorithm, they are able to judge the occurrence of dangerous situation; their essence is an estimation and approximation to the driving mode of surrounding automobile. The accuracy of this kind of estimation and approximation depends not only on the design of signal processing and algorithm, but also on the external environmental factors. For example, the active safety system based on camera sensor will generate false alarm and inspection omission when working at night; the active safety system based on radar sensor will present large deviation in the automobile lateral motion component.
Therefore, the defects of existing systems are mainly summed up into two points: 1. The algorithm dependence is high; 2. There are more conditions limited by the inherent characteristics of sensor. These defects will make the systems lose their functions and significances.